A simple example showing how to do result re ranking with RAG using Mastra, OpenAI, and PGVector.
rerank library for easy reranking of results
No description provided.
A a collection of languages stemmers and stopwords for Lunr Javascript library
The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.
No description provided.
A rag component for Convex.
Catalog plane foundation for Voyant. The shared cross-cutting infrastructure that vertical modules — `products`, `cruises`, `accommodations`, `charters`, `extras` — adopt to participate in a normalized discovery / overlay / snapshot / search surface.
A agent component for Convex.
Phase 2 of the catalog plane. Adds vector embeddings, AI-agent access patterns, and the MCP server scaffolding on top of the Phase 1 foundation in `@voyantjs/catalog`.
RaBitQ 1-bit quantized vector index in WebAssembly — 32× embedding compression with high-recall rerank, for browsers, Cloudflare Workers, Deno, and Bun
MemberJunction: Cohere AI Provider - Semantic reranking using Cohere's Rerank API
MCP server for semantic search of GitHub issues/PRs via Cloudflare Worker
No description provided.
Local cross-encoder reranker for Engram — runs mxbai-rerank-v1 via ONNX Runtime (no API calls)
Fireworks AI provider for chat completions, completions, and embeddings.
Retrivora AI is a plug-and-play AI engine for RAG chat experiences — generic vector DB + LLM provider, embeddable or standalone.
The official Pinecone TypeScript SDK for building vector search applications with AI/ML.
Extract clean, timestamped YouTube captions, subtitles, transcripts, and video metadata for AI summaries, RAG, search, and slide-ready workflows.
AI-powered knowledge graph platform with WebGL visualization
Local RAG MCP Server - Easy-to-setup document search with minimal configuration
Retrieval Augmented Generation for Cogitator AI agents
A simple example showing how to do result re ranking with RAG using Mastra, OpenAI, and PGVector.
A JavaScript library for Retrieval-Augmented Generation (RAG) within the QVAC ecosystem. Build powerful, context-aware AI applications with seamless document ingestion, vector search, and LLM integration.